{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2 as cv\n",
    "import ssl\n",
    "import json\n",
    "import base64\n",
    "import http.client as httplib\n",
    "import pandas as pd\n",
    "from shutil import copyfile\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = {\"Content-type\": \"application/json\",\n",
    "           \"X-Access-Token\": \"VfLe1Cnpc71kImp2CbIMMRsJh2TLSGtcXjMJ\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_names = {\n",
    "    'Brown' : ['brown', 'beige', 'cream', 'beige/cream'],\n",
    "    'Silver' : ['silver', 'grey', 'silver/grey'],\n",
    "    'Blue' : ['blue'],\n",
    "    'White' : ['white'],\n",
    "    'Black' : ['black'],\n",
    "    'Red' : ['red'],\n",
    "    'Green' : ['green'],\n",
    "    'Yellow' : ['yellow', 'orange'],\n",
    "    'Orange' : ['Orange']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_to_main = {\n",
    "    'White' : 'White',\n",
    "    'Blue' : 'Blue',\n",
    "    'Silver' : 'Silver',\n",
    "    'Black' : 'Black',\n",
    "    'Grey' : 'Silver',\n",
    "    'Sky blue' : 'Blue',\n",
    "    'Red' : 'Red',\n",
    "    'Beige' : 'Brown',\n",
    "    'Green' : 'Green',\n",
    "    'Yellow' : 'Yellow',\n",
    "    'Brown' : 'Brown',\n",
    "    'Orange' : 'Orange'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_colors = ['White', 'Blue', 'Silver', 'Black', 'Red', 'Brown', 'Green', 'Yellow', 'Orange']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['White', 'Blue', 'Silver', 'Black',\n",
    "        'Red', 'Green', 'Yellow', 'Brown', 'Orange']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "request_number = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotting(path, x1, y1, x2, y2):\n",
    "    print(x1, y1, x2, y2)\n",
    "    src_image = cv.imread(path)\n",
    "#     height, width = src_image.shape[:2]\n",
    "    crop_image = src_image[y1:y2, x1:x2]\n",
    "    \n",
    "    cv.imwrite('./a.jpg', crop_image)\n",
    "    plt.imshow(crop_image[:,:,::-1])\n",
    "    \n",
    "# plot_img('../my_data/img_data/car_imgs/'+'45732930/2.jpg')\n",
    "# plot_img('../my_data/img_data/car_imgs/45626893/0.jpg')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_img(path):\n",
    "    src_image = cv.imread(path)\n",
    "    plt.imshow(src_image[:,:,::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_connection():\n",
    "    conn = httplib.HTTPSConnection(\"dev.sighthoundapi.com\", \n",
    "        context=ssl.SSLContext(ssl.PROTOCOL_TLSv1_2))\n",
    "    return conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_box(path, color, conn):\n",
    "    \n",
    "    image_data = base64.b64encode(open(path, 'rb').read()).decode()\n",
    "    params = json.dumps({\"image\": image_data})\n",
    "    conn.request(\"POST\", \"/v1/recognition?objectType=vehicle\", params, headers)\n",
    "    response = conn.getresponse()\n",
    "    result = response.read()\n",
    "    json_obj = json.loads(result)\n",
    "#     print(json_obj)\n",
    "    if 'reasonCode' in json_obj and json_obj['reasonCode'] == 50202:\n",
    "        print(json_obj)\n",
    "        return 'TL'\n",
    "    global request_number\n",
    "    request_number += 1\n",
    "    if not json_obj or 'objects' not in json_obj or len(json_obj['objects']) < 1:\n",
    "        return False\n",
    "    annot = json_obj['objects'][0]['vehicleAnnotation']\n",
    "    if annot['recognitionConfidence'] < 0.70:\n",
    "        return False\n",
    "    \n",
    "    col = annot['attributes']['system']['color']['name']\n",
    "    conf = annot['attributes']['system']['color']['confidence']\n",
    "    if (col in color_names[color]) and conf < .5:\n",
    "        return False\n",
    "    vertices = annot['bounding']['vertices']\n",
    "    xy1 = vertices[0]\n",
    "    xy3 = vertices[2]\n",
    "    return xy1['x'], xy1['y'], xy3['x'], xy3['y']\n",
    "\n",
    "# get_box('../my_data/img_data/car_imgs/45505633/2.jpg', 'White', create_connection())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_folder(folder):\n",
    "    if not os.path.exists(folder):\n",
    "        os.makedirs(folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_images(IDs, color, conn):\n",
    "    boxes = {}\n",
    "    for ID in IDs:\n",
    "        count = 2\n",
    "        src = '../my_data/img_data/car_imgs/' + str(ID)\n",
    "        dst = '../color_data3/' + str(ID)\n",
    "        print(src)\n",
    "        num_files = len([name for name in os.listdir('.') if os.path.isfile(name)])\n",
    "        for i in range(num_files):\n",
    "            img_src = src + '/' + str(i)\n",
    "            if os.path.isfile(img_src+'.jpg'):\n",
    "                img_src += '.jpg'\n",
    "            elif os.path.isfile(img_src+'.png'):\n",
    "                img_src += '.png'\n",
    "            else:\n",
    "                continue         \n",
    "            \n",
    "            resp = get_box(img_src, color, conn)\n",
    "            if resp == 'TL':\n",
    "                global request_number\n",
    "                request_number = 0\n",
    "                conn.close()\n",
    "                print('Sleeping 5 min!!!!!!')\n",
    "                countdown(5*60)\n",
    "                conn = create_connection()\n",
    "            elif resp:\n",
    "                print('+', str(ID)+'/'+str(i))\n",
    "                boxes[str(ID) + '/' + str(i)] = resp, color\n",
    "                copyfile(img_src, dst+'.'+str(i)+'.jpg')\n",
    "                count -= 1\n",
    "            if count == 0:\n",
    "                break\n",
    "    return boxes\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['White', 'Blue', 'Silver', 'Black', nan, 'Grey', 'Carnelian red',\n",
       "       'Sky blue', 'Red', 'Beige', 'Green', 'Yellow', 'Purple', 'Brown',\n",
       "       'Golden', 'Orange', 'Pink',\n",
       "       'ado  Prod. year 2018  CategoryPickup   Fuel type Diesel  Engine\\xa0volume  2.8  Mileage 32685 km   Cylinders 4   Gear box typeAutomatic  Drive wheels  Doors 4/5       Wheel Left wheel  Color Black',\n",
       "       'ado  Prod. year 2017  CategorySedan    Fuel type Petrol  Engine\\xa0volume  3.6  Mileage 79093 km   Cylinders 6   Gear box typeAutomatic  Drive wheels Front  Doors 4/5       Wheel Left wheel  Color Blue'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../my_data/final.csv')\n",
    "df.Color.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85937, 14)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, color in enumerate(colors):\n",
    "    col_df = df.loc[df['Color'] == color]\n",
    "    col_df = col_df.sample(n = 100, replace = True)\n",
    "    col_df.to_csv('../chosedfs3/{}.csv'.format(color))\n",
    "    dfs.append(col_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, color in enumerate(colors):\n",
    "    col_df = pd.read_csv('../chosedfs2/{}.csv'.format(color))\n",
    "    dfs.append(col_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Category</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Interior color</th>\n",
       "      <th>VIN</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11975</td>\n",
       "      <td>45656196</td>\n",
       "      <td>BMW</td>\n",
       "      <td>320 Diesel</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>159000 km</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>11800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3134</td>\n",
       "      <td>45713196</td>\n",
       "      <td>MAZDA</td>\n",
       "      <td>323</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>236589 km</td>\n",
       "      <td>Manual</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Grey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15769</td>\n",
       "      <td>45753731</td>\n",
       "      <td>VOLKSWAGEN</td>\n",
       "      <td>Polo</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>194000 km</td>\n",
       "      <td>Manual</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19399</td>\n",
       "      <td>45101783</td>\n",
       "      <td>LAND ROVER</td>\n",
       "      <td>Discovery</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>135000  km</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>917.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2491</td>\n",
       "      <td>45715491</td>\n",
       "      <td>OPEL</td>\n",
       "      <td>Vectra</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>126000 km</td>\n",
       "      <td>Manual</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Price negotiable</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        ID Manufacturer       Model   Category     Mileage  \\\n",
       "0       11975  45656196          BMW  320 Diesel      Sedan   159000 km   \n",
       "1        3134  45713196        MAZDA         323  Hatchback   236589 km   \n",
       "2       15769  45753731   VOLKSWAGEN        Polo  Hatchback   194000 km   \n",
       "3       19399  45101783   LAND ROVER   Discovery       Jeep  135000  km   \n",
       "4        2491  45715491         OPEL      Vectra      Sedan   126000 km   \n",
       "\n",
       "  Gear box type Doors       Wheel Color Interior color  VIN  Leather interior  \\\n",
       "0     Tiptronic   4/5  Left wheel  Blue          Black  NaN                 1   \n",
       "1        Manual   4/5  Left wheel  Blue           Grey  NaN                 0   \n",
       "2        Manual   4/5  Left wheel  Blue          Black  NaN                 0   \n",
       "3     Tiptronic   4/5  Left wheel  Blue          Black  NaN                 1   \n",
       "4        Manual   4/5  Left wheel  Blue          Black  NaN                 0   \n",
       "\n",
       "              Price  Customs  \n",
       "0             11800      NaN  \n",
       "1              2000      NaN  \n",
       "2              1800      NaN  \n",
       "3             20000    917.0  \n",
       "4  Price negotiable      NaN  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Category</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Interior color</th>\n",
       "      <th>VIN</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51275</td>\n",
       "      <td>45640728</td>\n",
       "      <td>SUBARU</td>\n",
       "      <td>Forester</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>225685 km</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JF2SJARC5FH599028</td>\n",
       "      <td>1</td>\n",
       "      <td>Price negotiable</td>\n",
       "      <td>924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52461</td>\n",
       "      <td>45812216</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>GX 460</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>192896 km</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Grey</td>\n",
       "      <td>JTJBT20X480157130</td>\n",
       "      <td>1</td>\n",
       "      <td>Price negotiable</td>\n",
       "      <td>2455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78057</td>\n",
       "      <td>45761450</td>\n",
       "      <td>BMW</td>\n",
       "      <td>X5 M</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>65500  km</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>50000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59001</td>\n",
       "      <td>45776994</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Elantra</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>133502 km</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Beige</td>\n",
       "      <td>KMHDH4AE0EU183083</td>\n",
       "      <td>0</td>\n",
       "      <td>5500</td>\n",
       "      <td>584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79327</td>\n",
       "      <td>45652838</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>216208  km</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4/5</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KMHEC4A48DA083274</td>\n",
       "      <td>1</td>\n",
       "      <td>375</td>\n",
       "      <td>751.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        ID Manufacturer     Model Category     Mileage  \\\n",
       "0       51275  45640728       SUBARU  Forester     Jeep   225685 km   \n",
       "1       52461  45812216        LEXUS    GX 460     Jeep   192896 km   \n",
       "2       78057  45761450          BMW      X5 M     Jeep   65500  km   \n",
       "3       59001  45776994      HYUNDAI   Elantra    Sedan   133502 km   \n",
       "4       79327  45652838      HYUNDAI    Sonata    Sedan  216208  km   \n",
       "\n",
       "  Gear box type Doors       Wheel Color Interior color                VIN  \\\n",
       "0     Automatic   4/5  Left wheel  Blue            NaN  JF2SJARC5FH599028   \n",
       "1     Automatic   4/5  Left wheel  Blue           Grey  JTJBT20X480157130   \n",
       "2     Automatic   4/5  Left wheel  Blue          Black                NaN   \n",
       "3     Automatic   4/5  Left wheel  Blue          Beige  KMHDH4AE0EU183083   \n",
       "4     Automatic   4/5  Left wheel  Blue            NaN  KMHEC4A48DA083274   \n",
       "\n",
       "   Leather interior             Price  Customs  \n",
       "0                 1  Price negotiable    924.0  \n",
       "1                 1  Price negotiable   2455.0  \n",
       "2                 1             50000      NaN  \n",
       "3                 0              5500    584.0  \n",
       "4                 1               375    751.0  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = dels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "dels = []\n",
    "for dff in dfs:\n",
    "    dels += list(dff.ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "585\n"
     ]
    }
   ],
   "source": [
    "print(len(set(c).intersection(set(dels))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[df.ID not in dels]\n",
    "for dell in dels:\n",
    "    df = df[df.ID != dell]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85047, 14)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def countdown(t):\n",
    "    while t:\n",
    "        mins, secs = divmod(t, 60)\n",
    "        timeformat = '{:02d}:{:02d}'.format(mins, secs)\n",
    "        print(timeformat, end='\\r')\n",
    "        time.sleep(1)\n",
    "        t -= 1\n",
    "    print('Go API!\\n\\n\\n\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLOR:  White\n",
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      "../my_data/img_data/car_imgs/45747014\n",
      "{'error': 'ERROR_OVER_THROTTLE', 'reason': 'Please wait for a while', 'reasonCode': 50202}\n",
      "Sleeping 5 min!!!!!!\n",
      "Go API!\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "+ 45747014/2\n",
      "+ 45747014/3\n",
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      "+ 45604305/0\n",
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      "../my_data/img_data/car_imgs/45659768\n",
      "{'error': 'ERROR_OVER_THROTTLE', 'reason': 'Please wait for a while', 'reasonCode': 50202}\n",
      "Sleeping 5 min!!!!!!\n",
      "Go API!\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "../my_data/img_data/car_imgs/45663423\n",
      "+ 45663423/0\n",
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      "+ 45677240/0\n",
      "+ 45677240/1\n",
      "702.0958571434021 180\n",
      "COLOR:  Blue\n",
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      "+ 45771923/0\n",
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      "../my_data/img_data/car_imgs/45615371\n",
      "+ 45615371/0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+ 45615371/1\n",
      "../my_data/img_data/car_imgs/45586646\n",
      "+ 45586646/0\n",
      "+ 45586646/1\n",
      "../my_data/img_data/car_imgs/45481099\n",
      "+ 45481099/0\n",
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      "../my_data/img_data/car_imgs/45800910\n",
      "+ 45800910/1\n",
      "+ 45800910/2\n",
      "../my_data/img_data/car_imgs/45817555\n",
      "+ 45817555/0\n",
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      "+ 45682464/0\n",
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      "+ 45702515/0\n",
      "+ 45702515/1\n",
      "../my_data/img_data/car_imgs/45808120\n",
      "+ 45808120/0\n",
      "{'error': 'ERROR_OVER_THROTTLE', 'reason': 'Please wait for a while', 'reasonCode': 50202}\n",
      "Sleeping 5 min!!!!!!\n",
      "Go API!\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "../my_data/img_data/car_imgs/45744805\n",
      "+ 45744805/0\n",
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      "+ 45747155/2\n",
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      "+ 45818389/0\n",
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      "+ 45811902/0\n",
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      "../my_data/img_data/car_imgs/45224092\n",
      "../my_data/img_data/car_imgs/45626466\n",
      "../my_data/img_data/car_imgs/45758807\n",
      "../my_data/img_data/car_imgs/45783565\n",
      "../my_data/img_data/car_imgs/45452146\n",
      "../my_data/img_data/car_imgs/45762247\n",
      "../my_data/img_data/car_imgs/45745554\n",
      "../my_data/img_data/car_imgs/45664436\n",
      "../my_data/img_data/car_imgs/45638697\n",
      "../my_data/img_data/car_imgs/45784063\n",
      "88.85056662559509 261\n",
      "COLOR:  Orange\n",
      "../my_data/img_data/car_imgs/45814478\n",
      "../my_data/img_data/car_imgs/45540793\n",
      "../my_data/img_data/car_imgs/45800396\n",
      "../my_data/img_data/car_imgs/45803133\n",
      "../my_data/img_data/car_imgs/45803418\n",
      "../my_data/img_data/car_imgs/45792659\n",
      "../my_data/img_data/car_imgs/45740219\n",
      "../my_data/img_data/car_imgs/45732834\n",
      "../my_data/img_data/car_imgs/45720283\n",
      "../my_data/img_data/car_imgs/38197039\n",
      "../my_data/img_data/car_imgs/45809519\n",
      "../my_data/img_data/car_imgs/45803488\n",
      "../my_data/img_data/car_imgs/38197039\n",
      "../my_data/img_data/car_imgs/45622434\n",
      "../my_data/img_data/car_imgs/45740026\n",
      "../my_data/img_data/car_imgs/45791243\n",
      "../my_data/img_data/car_imgs/45792691\n",
      "../my_data/img_data/car_imgs/45760363\n",
      "../my_data/img_data/car_imgs/45809586\n",
      "../my_data/img_data/car_imgs/45806975\n",
      "../my_data/img_data/car_imgs/45803473\n",
      "../my_data/img_data/car_imgs/45803308\n",
      "../my_data/img_data/car_imgs/45490581\n",
      "../my_data/img_data/car_imgs/45803305\n",
      "../my_data/img_data/car_imgs/45732624\n",
      "../my_data/img_data/car_imgs/45803549\n",
      "../my_data/img_data/car_imgs/45803422\n",
      "../my_data/img_data/car_imgs/45641369\n",
      "../my_data/img_data/car_imgs/45814969\n",
      "../my_data/img_data/car_imgs/45735003\n",
      "../my_data/img_data/car_imgs/45193146\n",
      "../my_data/img_data/car_imgs/45810017\n",
      "../my_data/img_data/car_imgs/45803133\n",
      "../my_data/img_data/car_imgs/45809977\n",
      "../my_data/img_data/car_imgs/45766513\n",
      "../my_data/img_data/car_imgs/45641369\n",
      "../my_data/img_data/car_imgs/45733376\n",
      "../my_data/img_data/car_imgs/44644326\n",
      "../my_data/img_data/car_imgs/45593080\n",
      "../my_data/img_data/car_imgs/45762847\n",
      "../my_data/img_data/car_imgs/45803413\n",
      "../my_data/img_data/car_imgs/45735161\n",
      "../my_data/img_data/car_imgs/45643331\n",
      "../my_data/img_data/car_imgs/45733374\n",
      "../my_data/img_data/car_imgs/45803558\n",
      "../my_data/img_data/car_imgs/45822620\n",
      "../my_data/img_data/car_imgs/45732901\n",
      "../my_data/img_data/car_imgs/45766513\n",
      "../my_data/img_data/car_imgs/45733316\n",
      "../my_data/img_data/car_imgs/45803536\n",
      "../my_data/img_data/car_imgs/45803201\n",
      "../my_data/img_data/car_imgs/43104819\n",
      "../my_data/img_data/car_imgs/45732930\n",
      "../my_data/img_data/car_imgs/45735838\n",
      "../my_data/img_data/car_imgs/45789524\n",
      "../my_data/img_data/car_imgs/45803449\n",
      "../my_data/img_data/car_imgs/45803132\n",
      "../my_data/img_data/car_imgs/45803400\n",
      "../my_data/img_data/car_imgs/45735085\n",
      "../my_data/img_data/car_imgs/45340049\n",
      "../my_data/img_data/car_imgs/45732649\n",
      "../my_data/img_data/car_imgs/45732930\n",
      "../my_data/img_data/car_imgs/45805672\n",
      "../my_data/img_data/car_imgs/45773148\n",
      "../my_data/img_data/car_imgs/45803582\n",
      "../my_data/img_data/car_imgs/45735838\n",
      "../my_data/img_data/car_imgs/45822615\n",
      "../my_data/img_data/car_imgs/45809978\n",
      "../my_data/img_data/car_imgs/45774327\n",
      "../my_data/img_data/car_imgs/45803517\n",
      "../my_data/img_data/car_imgs/45803513\n",
      "../my_data/img_data/car_imgs/45803577\n",
      "../my_data/img_data/car_imgs/45810017\n",
      "../my_data/img_data/car_imgs/45823315\n",
      "../my_data/img_data/car_imgs/38197039\n",
      "../my_data/img_data/car_imgs/45732828\n",
      "../my_data/img_data/car_imgs/45799357\n",
      "../my_data/img_data/car_imgs/45734986\n",
      "../my_data/img_data/car_imgs/45762755\n",
      "../my_data/img_data/car_imgs/45823155\n",
      "../my_data/img_data/car_imgs/45734978\n",
      "../my_data/img_data/car_imgs/45595105\n",
      "../my_data/img_data/car_imgs/45280588\n",
      "../my_data/img_data/car_imgs/45735100\n",
      "../my_data/img_data/car_imgs/45791243\n",
      "../my_data/img_data/car_imgs/45803201\n",
      "../my_data/img_data/car_imgs/45733353\n",
      "../my_data/img_data/car_imgs/45732980\n",
      "../my_data/img_data/car_imgs/45280588\n",
      "../my_data/img_data/car_imgs/45791391\n",
      "../my_data/img_data/car_imgs/45803578\n",
      "../my_data/img_data/car_imgs/44295546\n",
      "../my_data/img_data/car_imgs/45723694\n",
      "../my_data/img_data/car_imgs/45732856\n",
      "../my_data/img_data/car_imgs/45732847\n",
      "../my_data/img_data/car_imgs/45732634\n",
      "../my_data/img_data/car_imgs/45817110\n",
      "../my_data/img_data/car_imgs/45803549\n",
      "../my_data/img_data/car_imgs/45733135\n",
      "../my_data/img_data/car_imgs/45733360\n",
      "87.17814135551453 261\n"
     ]
    }
   ],
   "source": [
    "boxes = {}\n",
    "for i, color in enumerate(colors):\n",
    "    col_df = dfs[i]\n",
    "    IDs = list(col_df.ID)\n",
    "    print('COLOR: ', color)\n",
    "    start = time.time()\n",
    "    conn = create_connection()\n",
    "    col_boxes = get_images(IDs, color_to_main[color], conn)\n",
    "    with open('./thirdtry/boxes_{}.json'.format(color), 'w') as f:\n",
    "        json.dump(col_boxes, f)\n",
    "        \n",
    "    boxes = {**boxes, **col_boxes}\n",
    "    end = time.time()\n",
    "    print(end-start, len(boxes))\n",
    "\n",
    "with open('./thirdtry/boxes.json', 'w') as f:\n",
    "    json.dump(boxes, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, color in enumerate(colors[:2]):\n",
    "    with open('boxes_{}.json'.format(color), \"r\") as read_file:\n",
    "        col_json = json.load(read_file)\n",
    "        boxes = {**col_json, **boxes}\n",
    "#         print(type(col_json))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./secondtry/finally_boxes.json', 'w') as f:\n",
    "    json.dump(boxes, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "655\n"
     ]
    }
   ],
   "source": [
    "print(len(boxes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((87, 53, 248, 197), 'Silver')\n",
      "87 53 248 197\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(boxes['45660870/1'])\n",
    "plotting('../color_data/45660870.1.jpg', *boxes['45660870/1'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {}\n",
    "for name, box in boxes.items():\n",
    "    if box[1] not in dic:\n",
    "        dic[box[1]] = 0\n",
    "    dic[box[1]] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'White': 74,\n",
       " 'Blue': 68,\n",
       " 'Silver': 77,\n",
       " 'Black': 74,\n",
       " 'Red': 87,\n",
       " 'Green': 68,\n",
       " 'Yellow': 45,\n",
       " 'Brown': 75,\n",
       " 'Orange': 87}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./firsttry/finally_boxes.json', \"r\") as read_file:\n",
    "    boxes_last1 = json.load(read_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./secondtry/boxes.json', \"r\") as read_file:\n",
    "    boxes_last2 = json.load(read_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = {**boxes_last1, **boxes_last2}\n",
    "len(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./finally_boxes.json', 'w') as f:\n",
    "    json.dump(final, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
